scholarly journals Metannot: A succinct data structure for compression of colors in dynamic de Bruijn graphs

2017 ◽  
Author(s):  
Harun Mustafa ◽  
André Kahles ◽  
Mikhail Karasikov ◽  
Gunnar Rätsch
Keyword(s):  
Data Structure ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Dynamic Data ◽  
De Bruijn Graphs ◽  
Dna And Rna ◽  
Succinct Data Structure ◽  
Dynamic Data Structure

AbstractMuch of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases.We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data.When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes.While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes.It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices.This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.

scholarly journals LOGAN: A framework for LOssless Graph-based ANalysis of high throughput sequence data

2017 ◽  
Author(s):  
Anthony Bolger ◽  
Alisandra Denton ◽  
Marie Bolger ◽  
Björn Usadel
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Sequence Data ◽  
De Bruijn Graph ◽  
Sequencing Data ◽  
Solanum Pennellii ◽  
Desktop Computer ◽  
De Bruijn Graphs ◽  
De Bruijn ◽  
Memory Efficient

AbstractRecent massive growth in the production of sequencing data necessitates matching improve-ments in bioinformatics tools to effectively utilize it. Existing tools suffer from limitations in both scalability and applicability which are inherent to their underlying algorithms and data structures. We identify the key requirements for the ideal data structure for sequence analy-ses: it should be informationally lossless, locally updatable, and memory efficient; requirements which are not met by data structures underlying the major assembly strategies Overlap Layout Consensus and De Bruijn Graphs. We therefore propose a new data structure, the LOGAN graph, which is based on a memory efficient Sparse De Bruijn Graph with routing information. Innovations in storing routing information and careful implementation allow sequence datasets for Escherichia coli (4.6Mbp, 117x coverage), Arabidopsis thaliana (135Mbp, 17.5x coverage) and Solanum pennellii (1.2Gbp, 47x coverage) to be loaded into memory on a desktop computer in seconds, minutes, and hours respectively. Memory consumption is competitive with state of the art alternatives, while losslessly representing the reads in an indexed and updatable form. Both Second and Third Generation Sequencing reads are supported. Thus, the LOGAN graph is positioned to be the backbone for major breakthroughs in sequence analysis such as integrated hybrid assembly, assembly of exceptionally large and repetitive genomes, as well as assembly and representation of pan-genomes.

scholarly journals Reference-free comparison of microbial communities via de Bruijn graphs

2016 ◽  
Author(s):  
Serghei Mangul ◽  
David Koslicki
Keyword(s):  
Microbial Communities ◽  
High Throughput Sequencing ◽  
Microbial Community Composition ◽  
De Bruijn Graph ◽  
Data Sets ◽  
Sequencing Data ◽  
De Bruijn Graphs ◽  
De Bruijn ◽  
The Individual ◽  
And Control

ABSTRACTMicrobial communities inhabiting the human body exhibit significant variability across different individuals and tissues, and are suggested to play an important role in health and disease. High-throughput sequencing offers unprecedented possibilities to profile microbial community composition, but limitations of existing taxonomic classification methods (including incompleteness of existing microbial reference databases) limits the ability to accurately compare microbial communities across different samples. In this paper, we present a method able to overcome these limitations by circumventing the classification step and directly using the sequencing data to compare microbial communities. The proposed method provides a powerful reference-free way to assess differences in microbial abundances across samples. This method, called EMDeBruijn, condenses the sequencing data into a de Bruijn graph. The Earth Mover's Distance (EMD) is then used to measure similarities and differences of the microbial communities associated with the individual graphs. We apply this method to RNA-Seq data sets from a coronary artery calcification (CAC) study and shown that EMDeBruijn is able to differentiate between case and control CAC samples while utilizing all the candidate microbial reads. We compare these results to current reference-based methods, which are shown to have a limited capacity to discriminate between case and control samples. We conclude that this reference-free approach is a viable choice in comparative metatranscriptomic studies.

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals Inferring species compositions of complex fungal communities from long- and short-read sequence data

2021 ◽  
Author(s):  
Yiheng Hu ◽  
Laszlo Irinyi ◽  
Minh Thuy Vi Hoang ◽  
Tavish Eenjes ◽  
Abigail Graetz ◽  
...  
Keyword(s):  
Community Composition ◽  
Pathogen Detection ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Composition Analysis ◽  
Sequencing Data ◽  
Species Classification ◽  
Shotgun Metagenomics ◽  
Query Coverage

Background: The kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. Results: We compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. Conclusion: Overall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

Phylogenomics of orchids and their mycorrhizal fungi : trees, diversity, and the pursuit of symbiosis

2019 ◽  
Author(s):  
◽  
Sarah Unruh
Keyword(s):  
Mycorrhizal Fungi ◽  
Phylogenetic Trees ◽  
High Throughput Sequencing ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Mycorrhizal Symbiosis ◽  
Sequencing Data ◽  
Phylogenetic Structure ◽  
University Of Missouri ◽  
Fungal Symbiosis

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Phylogenetic trees show us how organisms are related and provide frameworks for studying and testing evolutionary hypotheses. To better understand the evolution of orchids and their mycorrhizal fungi, I used high-throughput sequencing data and bioinformatic analyses, to build phylogenetic hypotheses. In Chapter 2, I used transcriptome sequences to both build a phylogeny of the slipper orchid genera and to confirm the placement of a polyploidy event at the base of the orchid family. Polyploidy is hypothesized to be a strong driver of evolution and a source of unique traits so confirming this event leads us closer to explaining extant orchid diversity. The list of orthologous genes generated from this study will provide a less expensive and more powerful method for researchers examining the evolutionary relationships in Orchidaceae. In Chapter 3, I generated genomic sequence data for 32 fungal isolates that were collected from orchids across North America. I inferred the first multi-locus nuclear phylogenetic tree for these fungal clades. The phylogenetic structure of these fungi will improve the taxonomy of these clades by providing evidence for new species and for revising problematic species designations. A robust taxonomy is necessary for studying the role of fungi in the orchid mycorrhizal symbiosis. In chapter 4 I summarize my work and outline the future directions of my lab at Illinois College including addressing the remaining aims of my Community Sequencing Proposal with the Joint Genome Institute by analyzing the 15 fungal reference genomes I generated during my PhD. Together these chapters are the start of a life-long research project into the evolution and function of the orchid/fungal symbiosis.

scholarly journals Highly accurate long-read HiFi sequencing data for five complex genomes

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ting Hon ◽  
Kristin Mars ◽  
Greg Young ◽  
Yu-Chih Tsai ◽  
Joseph W. Karalius ◽  
...  
Keyword(s):  
Sequence Data ◽  
Genome Structure ◽  
Data Sets ◽  
Sequencing Data ◽  
Complex Samples ◽  
Bioinformatic Tools ◽  
Long Reads ◽  
Sequencing Method ◽  
Sample Data ◽  
Long Read

AbstractThe PacBio® HiFi sequencing method yields highly accurate long-read sequencing datasets with read lengths averaging 10–25 kb and accuracies greater than 99.5%. These accurate long reads can be used to improve results for complex applications such as single nucleotide and structural variant detection, genome assembly, assembly of difficult polyploid or highly repetitive genomes, and assembly of metagenomes. Currently, there is a need for sample data sets to both evaluate the benefits of these long accurate reads as well as for development of bioinformatic tools including genome assemblers, variant callers, and haplotyping algorithms. We present deep coverage HiFi datasets for five complex samples including the two inbred model genomes Mus musculus and Zea mays, as well as two complex genomes, octoploid Fragaria × ananassa and the diploid anuran Rana muscosa. Additionally, we release sequence data from a mock metagenome community. The datasets reported here can be used without restriction to develop new algorithms and explore complex genome structure and evolution. Data were generated on the PacBio Sequel II System.

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